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Generating Adjectives to Express the Speaker’s Argumentative Intent

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  1. Generating Adjectives to Express the Speaker’s Argumentative Intent Author : Michael Elhadad Presented By Mithun Balakrishna

  2. Introduction • Express speaker’s intentions or argumentative orientation • Explanation component of ADVISOR • Example – “Course is very hard” – from the academic advisor does not refer to the property of the course but expresses his evaluation of the course

  3. Problems • Information cannot be found directly in the knowledge-base • Decision must be based on the speaker’s goals, a hearer model and the object being modified • Decisions interact with the lexical properties of adjectives, syntax of the clause and other factors like collocation

  4. Goal of the Paper • Input to a generator capable of producing argumentative usages to adjectives • Combining the many interacting factors constraining the adjective selection

  5. Previous Work • Referential Usage – speaker wants the hearer to identify some object • Attributive Usage – speaker wants to inform the hearer of object property • Satisfy pragmatic constraint – “Poor John was beaten………”

  6. Data and Motivation • Corpus – 40,000 Words, 700 occurrences of 150 distinct adjectives • Predicative and Attributive – 69 occurrences of 26 distinct adjectives • Cannot be found in knowledge-base describing courses

  7. Data and Motivation

  8. Information Needed to Choose an Adjective • Input cannot be attribute property P to a course C • Marked and Neutral – Data Structure is probably the hardest course and you would want to make sure that you could handle it. I really can’t tell you how difficult or easy they are. • Need to convey speaker argumentative intent

  9. Information Needed to Choose an Adjective • The description of this intent needs to be scalar and relative to a background • Absolute and Relative adjectives – a small elephant is a big animal a red box is as red as a red book • Relative adjectives depend not only on the object being modified (a good course is not good in the same sense as a good meal) but also depend on a model of the hearer

  10. Formal Representation of Argumentative Intent • Represent the argumentative orientation using scalar nature and relativity • The notation used is that of functional descriptions (FDs) used in Functional Unification Grammars (FUGs) (Kay, 1979, Elhadad, 1990a) • the {} notation indicates that focus is a pointer to the value of the attribute of the scope of the ao in the FD

  11. Formal Representation of Argumentative Intent

  12. Formal Representation of Argumentative Intent

  13. Lexical Representation of Adjectives • Information that needs to be present in the lexicon to describe adjectives • Lexical properties that constrain how adjectives can be used to convey an argumentative meaning

  14. Lexical Representation of Adjectives • Attributive or Predicative “An old friend”; “My friend is old” • Degree or Non-Degree “Hard Course”; “Required Course” • Marked or Neutral “Hard”; “Difficult” • Absolute or Relative “What is that course? It looked very interesting”; “If you’re good at math - that might be a good course to take”

  15. Lexical Representation of Adjectives • Similar to these selection restrictions but at the lexical level, lexical affinities or collocations “strongly recommended”; “very important” • The choice of the intensifier is constrained by the adjective

  16. Lexical Representation of Adjectives

  17. Interaction with other Surface Decision • Whether to use adjective at all to satisfy an argumentative intent and what adjective can be used when necessary • Verb lexically carries an argumentative evaluation of its object “I struggled with AI” (I took AI + I found AI hard.) “I enjoyed AI” (I took AI + I found AI interesting.) • “Data Structures follows Intro, and it is a very difficult course” – No verb to express both the notion of succession and evaluation of the course

  18. Interaction with other Surface Decision • The decision of using an adjective also interacts with the choice of the head of the noun phrase being modified. For example, proper nouns cannot be pre-modified by adjectives • The decision to explicitly express the relativity of the adjectival modification (does the generator produce AI is hard or AI is hard for an undergrad course) depends on what information is encoded in the reference variable and reference-set features

  19. Comments

  20. Adjectival Modification in Text Meaning Representation Authors : Victor Raskin and Sergei Nirenburg Presented By Mithun Balakrishna

  21. Introduction • MikroKosmos semantic analyzer - component of a knowledge-based machine translation system • The purpose and result of the MikroKosmos analysis process is the derivation of an interlingual representation for natural language inputs • The language in which these representations are expressed is called the "text meaning representation" (TMR) language • TMR is a frame-based language

  22. Goal of the Paper • Detecting and recording adjectival meaning • Compare with knowledge on adjectives in literature • Acquisition of lexical entries for adjectives

  23. The Ontological Approach to the Meaning of a Typical Adjective • A simple, prototypical case of adjectival modification is a scalar adjective, which modifies a noun both syntactically and semantically • Associates meaning with a region on a scale which is defined as the range of an ontological property

  24. The Ontological Approach to the Meaning of a Typical Adjective • Contribution of adjective to construction of TMR typically consists of inserting its meaning (a property-value pair) as a slot filler in a frame representing the meaning of the noun which this adjective syntactically modifies

  25. The Ontological Approach to the Meaning of a Typical Adjective

  26. The Ontological Approach to the Meaning of a Typical Adjective

  27. Semantic and Computational Treatment of Adjectives: Old and New Trends • The literature on adjectives shows a scarcity of systematic semantic analyses or lexicographic descriptions of adjectives • Focus on • taxonomies of adjectives • differences between attributive and predicative • syntactic transformations associated with various adjectival usages • on the qualitative/relative distinctions among adjectives • gradability/comparability of qualitative adjectives • Large-scale systems require entries for all lexical categories

  28. Semantic and Computational Treatment of Adjectives: Old and New Trends • Clear that the scalar/non-scalar dichotomy, and not the attributive~predicative distinction which dominates the literature, is the single most important distinction in semantic treatment of adjectives • The continuous numerical scales associated with the true scalars also render the issue of gradability and comparability rather trivial • Grain size of description • Principle of practical effability - stipulates that, in MT, the target language should be expected to have a corresponding adjective of a comparably large grain-size • If the context does not allow the analyzer to select a specific solution, a coarser-grain solution is preferred

  29. Semantic and Computational Treatment of Adjectives: Old and New Trends

  30. Non-Property Based Adjectival Modifications • Semantic treatment of adjectives which cannot be reduced to the standard property-based type of adjectival modification

  31. Non-Property Based Adjectival Modifications - Attitudes • Good is a scalar but unlike in the case of big, the LEX-MAP for Good does not contain a property-value pair that can be attached to the frame of the modified noun like house in the TMR • Instead, the meaning representation of good introduces an attitude on the part of the speaker with regard to the modified noun

  32. Non-Property Based Adjectival Modifications – Temporal Adjectives • The purely temporal knowledge in MikroKosmos is recorded with the meaning of the entire proposition, and adjective entries are not marked for it • Some temporal adjectives are presented as derived from adverbs rather than nouns • occasional visitor is analyzed as a rhetorical paraphrase of visit occasionally

  33. Non-Property Based Adjectival Modifications – Membership Adjectives • The lexical entry for this subclass focuses on two major elements: • whether the modified norm is a member of a certain set • whether the properties of this noun intersect significantly with those of the set members • authentic ,fake , nominal

  34. Non-Property Based Adjectival Modifications – Event Related Adjectives • To derive file semantic part of an adjectival entry from a verbal entry, first one must identify the case, or thematic role (such as agent, theme, beneficiary, etc.) filled by the noun modified by the adjective in question • Abuse – abusive speech; abusive man

  35. Non-Property Based Adjectival Modifications – Event Related Adjectives

  36. Non-Property Based Adjectival Modifications – Relative Adjectives

  37. Comments

  38. A Uniform Treatment of Pragmatic Inferences in Simple and Complex Utterances and Sequences of Utterances Author : Daniel Marcu and Graeme Hirst Presented By Mithun Balakrishna

  39. Introduction • Full account of natural language utterances cannot be given in terms of only syntactic and semantic phenomena • Need for – • Conversant’s belief and intentions – Scalar Implicature • Discourse expectations, Discourse plans and Discourse relations – Conversational Implicature

  40. Introduction • Defeasibility tricky notion to deal with • Difficult to formalize a cancellation of the presuppositions • Previous Work- • Analyze context • Extend boundaries • Assign status of de-feasible information

  41. Goal of Paper • Theoretical Framework – Stratified Logic, that can accommodate defeasible pragmatic inferences • An algorithm that computes the conversational, conventional, scalar, clausal, and normal state implicatures and the presuppositions associated with utterances

  42. Stratified Logic • Supports one type of indefeasible information • And two types of defeasible information • Infelicitously defeasible – “John regrets that Mary came to the party but she did not come” • Felicitously defeasible – “John does not regret that Mary came to the party because she did not come”

  43. Stratified Logic

  44. Stratified Logic

  45. Stratified Logic

  46. Algorithm • Input is a set of first-order stratified formulas that represent an adequate knowledge base • Builds the set of all possible interpretations for a given utterance using a generalization of the semantic tableau technique • The model ordering relation filters the optimistic interpretations • Defeasible inferences are checked

  47. Lexical Pragmatic Inferences

  48. Lexical Pragmatic Inferences

  49. Lexical Pragmatic Inferences

  50. Scalar Implicatures